# Datasheet — Azerbaijani Pretraining Corpus (v0) A cleaned, deduplicated, PII-redacted **~1.0 billion token** Latin-script Azerbaijani corpus for language-model pretraining, assembled from open multilingual web + encyclopedic sources with a reproducible [datatrove](https://github.com/huggingface/datatrove) pipeline. Structured as a [Datasheet for Datasets](https://arxiv.org/abs/1803.09010) (Gebru et al.). Built as Step 2 of the Azerbaijani LLM stack (see `CLAUDE.md`); the pipeline transfers to other Turkic languages. Token counts use the project's [`az_unigram_32k`](../tokenizer) SentencePiece tokenizer. --- ## At a glance | | | |---|---| | **Language** | Azerbaijani (`az` / `azj`), **Latin script only** | | **Documents** | 1,711,442 | | **Tokens** | **1,003,687,462** (`az_unigram_32k`; uint16) — train 998.6M / val 5.1M | | **Uncompressed text** | ~5.37 GB | | **Split** | 99.5% train / 0.5% val (document-level, seed 42) | | **Format** | nanoGPT `uint16` bins (`train.bin`, `val.bin`) + `meta.json`; `<|endoftext|>` (id 4) doc separator | | **License** | mixed per source (see Licensing) — redistribution as a derived corpus | | **Pipeline** | `data/` in this repo (fully reproducible) | --- ## Composition ### Sources & filtering funnel | source | HF repo (config) | docs after LID+script | after quality/PII | kept | **final (post-dedup)** | share | |---|---|---:|---:|---:|---:|---:| | CulturaX | `uonlp/CulturaX` (`az`) | 983,451 | 883,386 | 89.8% | 864,539 | 50.5% | | HPLT v2 | `HPLT/HPLT2.0_cleaned` (`azj_Latn`) | 988,573 | 904,204 | 91.5% | 697,071 | 40.7% | | mC4 | `allenai/c4` (`az`) | 414,310 | 308,400 | 74.4% | 145,512 | 8.5% | | Wikipedia | `wikimedia/wikipedia` (`20231101.az`) | 7,512 | 5,656 | 75.3% | 4,320 | 0.25% | | **Total** | | **2,393,846** | **2,101,646** | | **1,711,442** | 100% | Cross-source MinHash dedup removed **390,204 docs (18.6%)**. The largest cut was **mC4: 308,400 → 145,512 (−53%)** — CulturaX is derived from mC4+OSCAR, so most of our mC4 slice was duplicate CulturaX content and was correctly removed. HPLT (an independent crawl) overlapped little and mostly survived. > Provenance note: CulturaX rows carry their own `source` field ("mC4"/"OSCAR"); in the packed metadata > CulturaX documents therefore appear under their CulturaX-internal provenance rather than the label > `culturax`. The counts above use the intended source identity. ### Register Predominantly **web text** (CulturaX, HPLT, mC4 ≈ 99.7%) with a small **encyclopedic** slice (Wikipedia). This is a web-register corpus; it is not balanced for genre, domain, or dialect. ### What each instance is One instance = one document (a web page or article), as plain text. No labels, no annotations — this is an unlabeled pretraining corpus. --- ## Languages & script decision **Latin-script Modern (North) Azerbaijani only.** Cyrillic (U+0400–04FF) and Perso-Arabic (U+0600–06FF) text is dropped at the script-filter stage; we do **not** transliterate. Rationale (see repo README): Republic-of-Azerbaijan Azerbaijani has been officially Latin since ~2001 and all current production text is Latin. Cyrillic (legacy) and Perso-Arabic (South Azerbaijani) are different orthographies/distributions and out of scope for v0. Casing is preserved, including the semantically meaningful dotted/dotless-i distinction (`i`/`İ` vs `ı`/`I`). --- ## Collection process Documents were **streamed** from the public HuggingFace dataset repos above (no re-crawling). CulturaX is acceptance-gated (terms accepted + token); the others are open. We did not collect from individuals; all sources are existing public web/encyclopedic datasets. Collection date: 2026-06. OSCAR-2301 and MADLAD-400 were attempted but excluded from v0 (OSCAR gating pending; MADLAD config resolution impractical here). --- ## Preprocessing / cleaning Each document passed, in order (datatrove blocks unless noted; the Azerbaijani-specific pieces are custom): 1. **Script filter** *(custom)* — keep documents ≥85% Latin letters, <5% Cyrillic/Arabic, ≥200 chars. 2. **Language ID** — fastText `lid.176`, keep `az` at ≥0.65 confidence (distinguishes Azerbaijani from Turkish cleanly: az→`az` @1.000, not `tr`). 3. **Gopher repetition filter** — drop documents dominated by repeated lines / n-grams. 4. **Gopher quality filter** — length, mean word length, symbol ratios, and an **Azerbaijani stop-word** presence check *(custom ~50-word list; datatrove's default is English)*. 5. **C4 quality filter** — per-line web cruft (too-short lines, JS, lorem-ipsum, stray braces). 6. **PII redaction** — emails and public IP addresses replaced with placeholders. 7. **MinHash near-duplicate removal** — across **all** sources jointly (5-grams, 14×8 LSH, 64-bit), tokenized with a custom Azerbaijani whitespace/regex word tokenizer (no spaCy). 8. **Tokenize + pack** — `az_unigram_32k`, document-shuffled (buffer 100k, seed 42), `uint16` bins. Word/sentence tokenization for the filters uses a custom regex tokenizer faithful to Gopher's original whitespace word-counting and correct for agglutinative Latin Azerbaijani. --- ## Uses - **Intended:** pretraining / continued-pretraining of Azerbaijani (or multilingual) language models; tokenizer evaluation; corpus linguistics; a base to mix with other languages. - **Out of scope:** anything requiring labels (it has none); high-stakes or factual-authority use (it is unfiltered-for-truth web text); Cyrillic/Perso-Arabic Azerbaijani (excluded); dialectal balance. --- ## Limitations & biases (honest) - **Web-register skew.** ~99.7% web text → boilerplate, SEO, commercial, and news-heavy distributions; Wikipedia is the only encyclopedic counterweight (0.25%). Not genre-balanced. - **Size.** ~1.0B tokens is solid for a low-resource language but ~8 tokens/param for a 124M model (Chinchilla-optimal ≈ 20) — expect to overtrain or use as continued-pretraining data. - **Quality is heuristic, not human-reviewed.** Gopher/C4 rules catch gross junk; subtle low-quality, toxicity, social bias, and factual errors in web text are **not** removed. No toxicity classifier was run. - **Lineage overlap.** CulturaX/mC4 share mC4 lineage; dedup removes near-duplicates but exact provenance weighting is approximate. CulturaX dominates (50.5%), so its biases dominate. - **PII redaction is regex-level** (emails/IPs) — not a guarantee; names and other PII in public web text may remain. - **Latin-only** by design — excludes Cyrillic and South Azerbaijani (Perso-Arabic) speakers' text. --- ## Distribution & licensing This is a **derived** corpus; per-source upstream terms apply: | source | license | |---|---| | CulturaX | ODC-BY + per-document terms | | HPLT v2 | per HPLT release terms (CC0 metadata) | | mC4 / C4 | ODC-BY | | Wikipedia | CC-BY-SA 4.0 | The CC-BY-SA (Wikipedia) component implies share-alike obligations on redistribution. Consult each upstream dataset's terms before redistributing. We redistribute only as documented derived artifacts/pointers. --- ## Reproduction ```bash # (gated CulturaX needs: hf auth login + accept terms) .venv-data/bin/python data/download/fetch.py --sources culturax,hplt,mc4,wikipedia --max-retries 4 .venv-data/bin/python data/filter/clean.py --sources all .venv-data/bin/python data/filter/dedup.py .venv-data/bin/python data/pack/tokenize_pack.py .venv-data/bin/python data/stats/report.py ``` Exact configs, seeds, and the dedup `tasks=1` determinism fix are in `data/` and `experiments/RUN_LOG.md`. Pipeline payloads are gitignored; counts/decisions are committed. ## Maintenance v0, 2026-06. Planned: add OSCAR-2301 (gating pending) and MADLAD-400; broaden register; build a held-out eval that spans registers. Contact: project maintainer (see repo). ## Citation A formal citation will accompany the HuggingFace release. For now cite the repository and the upstream datasets (CulturaX, HPLT v2, mC4/C4, Wikipedia).